Summary1. Species' distribution modelling relies on adequate data sets to build reliable statistical models with high predictive ability. However, the money spent collecting empirical data might be better spent on management. A less expensive source of species' distribution information is expert opinion. This study evaluates expert knowledge and its source. In particular, we determine whether models built on expert knowledge apply over multiple regions or only within the region where the knowledge was derived. 2. The case study focuses on the distribution of the brush-tailed rock-wallaby Petrogale penicillata in eastern Australia. We brought together from two biogeographically different regions substantial and well-designed field data and knowledge from nine experts. We used a novel elicitation tool within a geographical information system to systematically collect expert opinions. The tool utilized an indirect approach to elicitation, asking experts simpler questions about observable rather than abstract quantities, with measures in place to identify uncertainty and offer feedback. Bayesian analysis was used to combine field data and expert knowledge in each region to determine: (i) how expert opinion affected models based on field data and (ii) how similar expert-informed models were within regions and across regions. 3. The elicitation tool effectively captured the experts' opinions and their uncertainties. Experts were comfortable with the map-based elicitation approach used, especially with graphical feedback. Experts tended to predict lower values of species occurrence compared with field data. 4. Across experts, consensus on effect sizes occurred for several habitat variables. Expert opinion generally influenced predictions from field data. However, south-east Queensland and north-east New South Wales experts had different opinions on the influence of elevation and geology, with these differences attributable to geological differences between these regions. 5. Synthesis and applications. When formulated as priors in Bayesian analysis, expert opinion is useful for modifying or strengthening patterns exhibited by empirical data sets that are limited in size or scope. Nevertheless, the ability of an expert to extrapolate beyond their region of knowledge may be poor. Hence there is significant merit in obtaining information from local experts when compiling species' distribution models across several regions.

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Climate change is likely to affect plants in multiple ways, but predicting the consequences for habitat suitability requires a process-based understanding of the interactions. This is at odds with existing approaches that are mostly phenomenological and largely restricted to predicting the effects of changing temperature and rainfall on species distributions at a coarse spatial scale. We examine the multiple effects of climate change, including predicting the effects of altered flood regimes and land-use change, on the potential distribution of the invasive riparian species lippia (Phyla canescens) across a 26 000 km 2 catchment in eastern Australia. We determined habitat suitability for lippia by combining process-understanding of experts and an eco-physiological bioclimatic model within a Bayesian belief network. The bioclimatic model predicted substantial changes in habitat suitability by 2070 under both a wetter (Echam Mark 3) and drier (Hadley Centre Mark 2) climate change scenario, but only the more likely drier scenario reduced suitability in our test region. The area suitable for lippia was predicted to increase at least threefold with increased flooding under a wet climate scenario, although this would be partially negated by land-use change to cultivation. The region would become unsuitable to lippia with reduced flooding under a drier scenario irrespective of land-use changes, although existing populations would persist if grazing persisted. Independent field validation verified model structure and parameterization, and therefore the opinion of experts, but identified site-scale deficiencies in the available environmental data layers. Model predictions suggest that adaptation options for managing lippia will be greatly reduced under a drying scenario, but identify potential restoration opportunities under either scenario. This work highlights the value of predictive models that incorporate process-understanding at sufficiently fine spatial resolution to capture the important processes underpinning habitat suitability.

Two broad aims drive weed science research: improved management and improved understanding of weed biology and ecology. In recent years, agricultural weed research addressing these two aims has effectively split into separate subdisciplines despite repeated calls for greater integration. Although some excellent work is being done, agricultural weed research has developed a very high level of repetitiveness, a preponderance of purely descriptive studies, and has failed to clearly articulate novel hypotheses linked to established bodies of ecological and evolutionary theory. In contrast, invasive plant research attracts a diverse cadre of nonweed scientists using invasions to explore broader and more integrated biological questions grounded in theory. We propose that although studies focused on weed management remain vitally important, agricultural weed research would benefit from deeper theoretical justification, a broader vision, and increased collaboration across diverse disciplines. To initiate change in this direction, we call for more emphasis on interdisciplinary training for weed scientists, and for focused workshops and working groups to develop specific areas of research and promote interactions among weed scientists and with the wider scientific community.